Stephen Becker
Assistant Professor

Room number: ECOT 338

UPDATE: July 2019, I am moving my office from ECOT 231 to ECOT 338.

I joined the department of Applied Math in fall 2014. Previously I was a Herman Goldstine Postdoctoral fellow in Mathematical Sciences at IBM Research in Yorktown Heights, NY, and a postdoctoral fellow via the Fondation Sciences Mathématiques de Paris at Paris 6 (JLL lab), after doing my doctoral work at Caltech.

Broadly speaking, our group is interested in information extraction from various types of datasets. We are part of a hybrid field combining applied math with computer science and signal processing techniques. Some specific topics we research are:

  • Optimization: first-order methods, quasi-Newton methods, primal-dual algorithms, convex analysis.
    • Types of problems: from computational imaging, and semi-definite programs (from relaxations, or from robust PCA)
    • Mathematical applications: compressed sensing and variants, matrix completion and variants (robust PCA…), non-negative matrix factorization and end-member detection, sparse SVM
  • Numerical linear algebra: randomization and its interplay with optimization methods
  • Sampling theory: how to make the best use of your resources when confronted with big data
  • Physical applications: radar ADC using compressed sensingquantum tomography, MRI, medical imaging, IMRT, renewable energy, big-data
    • Recent applications (2015--2018) have been in super-resolution (optical) microscopy and photo-acoustic microscopy

To get a more specific idea of the research our group does, here are some topics we're doing in 2018:

  • Parametric and compressive estimation, for phase retrieval (Jessica) in x-ray imaging, and for discovering archaeological ruins (Abby) in radar imaging without creating a DEM
  • Theoretical machine learning: sub-sampling and sketching (Farhad, Eric)
  • Avoiding/analyzing saddle points in non-convex optimization: for biconvex programming in program analysis and/or controls (Jessica), and for dictionary learning and neural network learning (Leo)
  • Improving accuracy of sparse estimation using mixed-integer programming (Eric, Leo)
  • Efficient computation of the cross-ambiguity function (CAF) for signal processing, to estimate time-of-arrival of radar signals (James)
  • Randomized algorithms for numerical linear algebra and optimization (James, Derek)
  • Optimization algorithms in general, and ill-conditioning and pre-conditioning (James, Jessica, Osman)
  • Efficient algorithms for GPUs (James, Derek, Jessica)
  • Tensor decompositions (Osman, Derek)
  • Robust estimation (Richie)
  • Misc imaging applications (for optical super-resolution, with Carol Cogswell's group in ECEE; and for photo-acoustic super-resolution, with Todd Murray's group in Mech E)
  • Stochastic variance reduction methods for non-linear inverse problems
  • Remote sensing of the Chesapeake bay (Cheryl)
  • Behavior genetics (Richard, Farhad)


  • Plans for Summer 2019
    • Internships to be announced soon
  • Winter 2018/2019
    • Marc Thomson and Richard Border are doing their Masters theses with the group
    • Liam Madden has joined the group (working also with Emiliano Dall'Anese)
    • Richie Clancy is working on robust optimization
  • July 2018: Becker is PI on a 3-year $150k NSF grant in computational math
  • July 2018: Becker is Co-PI on Prof. Ken Jansen's ALCF project
    • This gives us early access to the new Aurora supercomputer (the nation's first exascale computer)
  • Summer 2018 activities:
  • Spring 2018: Farhad Pourkamali-Anaraki (PhD then Postdoc in the group) accepts a tenure-track professor job at U Mass Lowell's computer science deparment
  • May 2017: Farhad Pourkamali-Anaraki receives his PhD in electrical engineering
  • May 2017: Derek Driggs receives his Masters in applied math, and heads to Cambridge for his PhD
  • April 2017: Derek Driggs wins the Gates Cambridge scholarship (fully funded PhD at Cambridge, equivalent to a Rhodes scholar for Oxford)
  • Summer 2016:
  • July 2015, Becker awarded the Beal-Orchard-Hays prize
    • along with Michael Grant and Emmanuel Candes. The BOH prize is awarded every 3 years for outstanding optimization software


Our research group website has more information on research topics.

(note: there is also some interesting description of our work at our old website. The only website that we update regularly is the research group website)


January 2018, I am one of four founding members of the new Imaging Science center in the engineering college. Here is the new Imaging Science IRT website.

You may be interested in joining the Colorado data science team.

We run a Statistics, Optimization and Machine Learning seminar (Fall 2018, this is usually 3:30 Tuesdays at Newton lab). Anyone is welcome to show up.

(to receive announcements about talks at the seminar, please sign up for the StatOptML google group).

For K-12 students and educators interested in partnering with CU

Some resources:

Thinking about a PhD in Applied Math at CU?

Thinking about applied math in general?